The digital home of the future is envisioned to be a mix of sensing and computing infrastructure that seamlessly interacts with the user to enable a wide range of personalized digital home applications and services. Examples include recommending content by identifying who is watching television, personalizing the settings of an appliance based on who is using it, and personalizing the cooking experience based on who is performing the activity in the home. A key component of many such applications is a non-intrusive and seamless user identification and tracking technique to personalize the experience for the user.
Existing approaches for user tracking and identification are cumbersome as they are either limited to individual devices that require explicit feedback from the user or make use of invasive sensors like microphones and cameras. Approaches requiring users to log in or pick a profile are limited to a handful of devices in the home, like smart TVs and media devices, and are often from the same manufacturer. Such approaches cannot provide seamless user tracking and identification across multiple heterogeneous devices in the home. Other approaches that require the installation of sensors like cameras and microphones raise several privacy concerns and are fragile to environmental conditions like poor lighting or background noise.